model = dict(
    type="MaskRCNN",
    pretrained="open-mmlab://detectron2/resnet50_caffe",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=False),
        norm_eval=True,
        style="caffe",
    ),
    neck=dict(
        type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5
    ),
    rpn_head=dict(
        type="RPNHead",
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type="AnchorGenerator",
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0],
        ),
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", loss_weight=1.0, beta=0.1111111111111111),
    ),
    roi_head=dict(
        type="StandardRoIHead",
        bbox_roi_extractor=dict(
            type="SingleRoIExtractor",
            roi_layer=dict(
                type="RoIAlign", output_size=7, sampling_ratio=2, aligned=False
            ),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
        ),
        bbox_head=dict(
            type="Shared2FCBBoxHead",
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type="DeltaXYWHBBoxCoder",
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2],
            ),
            reg_class_agnostic=False,
            loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type="SmoothL1Loss", loss_weight=1.0, beta=1.0),
        ),
        mask_roi_extractor=dict(
            type="SingleRoIExtractor",
            roi_layer=dict(
                type="RoIAlign", output_size=14, sampling_ratio=2, aligned=False
            ),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
        ),
        mask_head=dict(
            type="FCNMaskHead",
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=80,
            loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0),
        ),
    ),
)
train_cfg = dict(
    rpn=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            match_low_quality=True,
            ignore_iof_thr=-1,
        ),
        sampler=dict(
            type="RandomSampler",
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False,
        ),
        allowed_border=-1,
        pos_weight=-1,
        debug=False,
    ),
    rpn_proposal=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0,
    ),
    rcnn=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.5,
            neg_iou_thr=0.5,
            min_pos_iou=0.5,
            match_low_quality=True,
            ignore_iof_thr=-1,
        ),
        sampler=dict(
            type="RandomSampler",
            num=512,
            pos_fraction=0.25,
            neg_pos_ub=-1,
            add_gt_as_proposals=True,
        ),
        mask_size=28,
        pos_weight=-1,
        debug=False,
    ),
)
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0,
    ),
    rcnn=dict(
        score_thr=0.05,
        nms=dict(type="nms", iou_threshold=0.5),
        max_per_img=100,
        mask_thr_binary=0.5,
    ),
)
dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True, with_mask=True, poly2mask=False),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(
        type="Normalize",
        mean=[103.53, 116.28, 123.675],
        std=[1.0, 1.0, 1.0],
        to_rgb=False,
    ),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels", "gt_masks"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(
                type="Normalize",
                mean=[103.53, 116.28, 123.675],
                std=[1.0, 1.0, 1.0],
                to_rgb=False,
            ),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type="CocoDataset",
        ann_file="data/coco/annotations/instances_train2017.json",
        img_prefix="data/coco/train2017/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="LoadAnnotations", with_bbox=True, with_mask=True, poly2mask=False
            ),
            dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
            dict(type="RandomFlip", flip_ratio=0.5),
            dict(
                type="Normalize",
                mean=[103.53, 116.28, 123.675],
                std=[1.0, 1.0, 1.0],
                to_rgb=False,
            ),
            dict(type="Pad", size_divisor=32),
            dict(type="DefaultFormatBundle"),
            dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels", "gt_masks"]),
        ],
    ),
    val=dict(
        type="CocoDataset",
        ann_file="data/coco/annotations/instances_val2017.json",
        img_prefix="data/coco/val2017/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="MultiScaleFlipAug",
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type="Resize", keep_ratio=True),
                    dict(type="RandomFlip"),
                    dict(
                        type="Normalize",
                        mean=[103.53, 116.28, 123.675],
                        std=[1.0, 1.0, 1.0],
                        to_rgb=False,
                    ),
                    dict(type="Pad", size_divisor=32),
                    dict(type="ImageToTensor", keys=["img"]),
                    dict(type="Collect", keys=["img"]),
                ],
            ),
        ],
    ),
    test=dict(
        type="CocoDataset",
        ann_file="data/coco/annotations/instances_val2017.json",
        img_prefix="data/coco/val2017/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="MultiScaleFlipAug",
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type="Resize", keep_ratio=True),
                    dict(type="RandomFlip"),
                    dict(
                        type="Normalize",
                        mean=[103.53, 116.28, 123.675],
                        std=[1.0, 1.0, 1.0],
                        to_rgb=False,
                    ),
                    dict(type="Pad", size_divisor=32),
                    dict(type="ImageToTensor", keys=["img"]),
                    dict(type="Collect", keys=["img"]),
                ],
            ),
        ],
    ),
)
evaluation = dict(metric=["bbox", "segm"])
optimizer = dict(type="SGD", lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.001, step=[8, 11]
)
total_epochs = 12
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type="TextLoggerHook")])
dist_params = dict(backend="nccl")
log_level = "INFO"
load_from = None
resume_from = None
workflow = [("train", 1)]
work_dir = "./work_dirs/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1"
gpu_ids = range(0, 1)
